### Textbook Topics

I am currently working on an introductory textbook and some additional materials (including textbook/class materials for an

**intermediate**course). I have a question for you.

Set aside any preconceptions about what

**must**be in an introductory (or intermediate) book on statistics. What

**should**be in such a book? Introductory stats books still suffer from being very 1950's oriented in terms of topics, emphasis, lack of computing, etc. (If you think computing should still play no role in such a book, let me know that, too.) Statistics has changed a lot over the decades, should not the books change, too?

If you are a statistician, what is important that is missing? If you are from another field, what do you find missing or what should not be there? Please let me know if you are a statistician or not, too, that would help.

Any input would be great, and be really appreciated! Thanks!

Crossposted at

**stat_geeks**

statzmanallogenesIf you have any examples--how have you seen software integrated in an especially good way? I guess my concern with software integration is that big publishers can make supplements for textbooks (so they can be changed as software evolves or for different packages) but this is for a smaller publisher and that is not possible. Any material will have to be in a single volume. Do you think it works better fully integrated in text or as separate chapters/sections?

This may be a side point, but one goal for this project is to make low-cost textbooks that do not get new editions every single year. That raises costs to students who can't find used copies. (Which, of course, is why the bigger publishers do it.)

Thanks!

statzmanThis book may serve as a good example:

http://www.pearsonhighered.com/educ

Each chapter has three main components: a) description of a statistical concept; b) a case study, where this statistical method is utilized to solve a real-life problem - this section also contains a partial write-up of the results in a proper academic journal format; c) syntax (code) for SAS and SPSS, that can be used to conduct the described analyses.

As for your last point - I think that neither statistical methods nor computer code used for statistical computing change very often, so there is no real need to publish a new edition every year... unless you want to rip-off some rich college kids))

allogenesAnd yeah, we don't expect a lot of changes every year, but we're trying to work on a line of books that old timers like me remember: ones that basically have one or two editions in total and last for a decade. Over the last decade software has changed. We're leaning toward using R as a core, and building a specific package to implement the text.

And yes; we're avoiding ripping students off. :-)

issac_spinozaalso, I have not seen any intro. textbooks that cover linear regression from a Bayesian framework. we always assume a classical one -- with the response that we don't want to confuse the students. My questions is why not introduce them to both frameworks and let them decide which is better for a particular problem.

also, emminant statisticians acknowledge problems with our introductory discussions with type I and type II error. i.e. what we say on the subject is not fully accurate. I think that someone should go back to the current statistical literature and correct this.

Why not introduce "advanced" computational topics at a simple level i.e. bootstraping.

if you are creating suplementary material as well I would say simple material on how to use R. most books on the topic are too sophisticated for an introductory user or are out-of-date or both.

last, but not least, better problems for the students to work through. some of the current problems are silly and are from the era of calculators and hand-calculations.

p.s. I am a statistician and a graduate student in statistics. anyways, HTH

allogenesI am tending toward introducing the bootstrap and randomization tests early on to give a good general tool, and then introduce other methods later. The "chemically pure" Bayesians I have spoken to, however, hate that idea. One put it this way: "bootstrapping is frequentist statistics run amok." So there does appear to be an issue there. Not sure how to deal with those sorts of issues...

issac_spinozaalso for an interesting and slightly more theoretical, at least by North American standards, 1st year statistics text book I suggest looking at Jeffery Rosenthal's book.

btw, apearently it is possible to have a Bayesian bootstrap. I personally don't know much about how it works.

I would also get rid of the silly standrd normal tables from teh 50's that everyone seems to put in their books.

these days very few people, at least in north American and i would suspect Europe do not have access to a computer, often a laptop.

also you need to decide and that would help with your content to what level you want to pitch the book i.e. to a business stats course, psychological stats course, or a regular biology / theoretical stats course.

HTH

allogenesI know from some survey work we've done, that a book based on bootstraps will have problems from the teachers; but a Bayesian book has different sorts of problems.

So I guess my real question is "what orientation do people want to see in such a book?" It sounds to me like you would prefer something eclectic and not married to real foundational issues. That seems to be the preference of applied statisticians I have surveyed. Oddly, social science people want a more Bayesian orientation but then get upset when probability notation/math shows up (go figure!). My theoretical statistician contacts appear to prefer a strong Bayesian foundation and less computation. As it is a general intro book, with a data focus, that is not going to happen. :-)

And yeah, computation will be embedded and tables removed. I'll look up the Rosenthal book. Thanks!

notebuyerallogenes